1. Field of the Invention
A method for automatically classifying a citation is disclosed with particular relevance to biomedical journal articles.
2. Description of Related Art
Evaluating the quality and impact of the scientific literature with citation count assumes that a citation is an indicator of quality. This is not necessarily true since a citation may serve many purposes unrelated to recognizing the value, rigor, or authority of the cited paper [1-3]. Cited papers may provide background information or acknowledge prior work that influenced the current work. Moreover, citations may serve non-scientific purposes due to social-psychological factors [4-6]. Thus, a citation is a subjective, indirect quality measure that does not have a single unambiguous use. On the other hand, a citation may criticize another work and not be an endorsement. Garfield created one of the earliest lists for the many possible reasons for a citation [7]:                1. Paying homage to pioneers        2. Giving credit to related work        3. Identifying methodology, equipment, etc.        4. Providing background reading        5. Correcting one's own work        6. Correcting the work of others        7. Criticizing previous work        8. Substantiating claims        9. Alerting to forthcoming work        10. Providing leads to poorly disseminated, poorly indexed, or uncited work        11. Authenticating data and classes of fact (physical constants, etc.)        12. Identifying original publications in which an idea or concept was discussed        13. Identifying original publication or other work describing an eponymic concept or term        14. Disclaiming work or ideas of others (negative claims        15. Disputing priority claims of others (negative homage)        
Previous work has attempted to automatically classify citations according to the purpose of the citation [8-10]. Teufel automatically classified citation function based on cue phrases and a part-of-speech based recognizer [10]. Citations were assigned to one of twelve categories that reflected whether the citation described a weakness in the cited paper, compared or contrasted the work, praised or described an influential aspect of the work, or was neutral. The corpus contained conference articles in computational linguistics from the Computation and Language E-Print Archive (http://xxx.lanl.gov/cmp-lg), and the evaluation corpus contained 2829 citations from 116 articles. The corpus was manually labeled according to a classification scheme of 12 categories, and performance was evaluated by using the IBk algorithm as the learning method which is a k-nearest neighbor classifier. The results yielded Kappa and Macro-F values of 0.57, and percentage accuracy was 0.77. When the classifications were combined into the four general categories, Kappa was 0.59, Macro-F was 0.68, and percentage accuracy was 0.79.
Garzone and Mercer [8] proposed another method for automatically classifying citations. They believed that scientific writing utilizes certain phrases for persuasion that indicate the underlying rhetorical purpose of a citation and that citations can be classified with these phrases. Linguistic cues or phrases were manually identified from Physics and Biochemistry articles. For example, a citation in the results section containing the words “postulated”, “reads”, or “reported” was classified into a specific category. Their parser consisted of lexical rules based on cue words and grammar-like parsing rules to match sophisticated patterns. The classification scheme contained 35 categories with 195 lexical rules and 14 parsing rules.
Automatically classifying citations could improve citation indexers since the nature of the relationship between articles would be known. Researchers and users could determine if an article criticizes, praises, builds upon, or compares itself to a cited article [10]. Current indexers find articles citing a given article but would be more helpful if they could identify articles using similar techniques or ones presenting conflicting results [9]. Automatic classification could also make large databases of articles more manageable by identifying related articles and performing information extraction or text summarization [9].
Another potential benefit of classifying citations is improving citation metrics such as journal impact factor and article citation count. The performance of existing evaluation methods may improve if instrumental citations could be reliably distinguished from non-instrumental ones. Modified versions of citation count and journal impact factor will be better quality metrics if they only counted citations to papers that played a central role in the generation of the hypothesis or provided necessary foundational knowledge.